The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Hybrid Explainable AI Framework Applied to Global and Local Facial Expression Recognition
Open accessAbstract
Facial Expression Recognition (FER) systems have many applications such as human behavior understanding, human machine interface, video games and health monitoring. The main advantage of the traditional white box methods is their explainability. However, the accuracy of recognition of these methods is completely reliant on the extracted features. On the other hand, the use of deep neural networks has advantage regarding the overall precision compared to traditional methods. Indeed, they are considered as black box methods and thus suffer from lack of reliability and explainability. In this work, we introduce a hybrid AI explainable framework (HEF) composed of a main functional pipeline comprising a Convolutional Neural Network (CNN) to classify input images and an explainable pipeline using Facial Action Units and application agnostic models LIME providing more useful data allowing to explain the obtained results and reinforce the decision provided by the main functional pipeline. The proposed HEF has been validated on the CK+ dataset and shows very promising results in terms of explainability of the obtained results.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhang, Z., & Sabuncu, M. R. (2018) · arXiv · doi:10.48550/arxiv.1805.07836 · Cited by 1,907 · cited 1×
“Both CNN and MLP models were trained using 80 epochs via the Adam [19] optimization algorithm, as well as the categorical cross-entropy loss function [20].”
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Flagged first, then by citations
Research works printed but not linked: 24
- 10.1109/TCYB.2017.2788081 · DOI · Paper page
- 10.1109/CVPR46437.2021.00618 · DOI
- masís, 0, Interpretable Machine Learning with Python
- 10.1007/s11263-019-01228-7 · DOI · Paper page
- lundberg, 2017, A Unified Approach to Interpreting Model Predictions, arXiv 1705 07874 [cs stat], arxiv: 1705.07874 · arXiv
- 10.1145/2939672.2939778 · DOI · Paper page
- 10.1109/CVPRW.2010.5543262 · DOI · Paper page
- mehtab, 2020, Face Detection Using OpenCV and Haar Cascades Classifiers
- 10.1609/aaai.v34i07.6963 · DOI
- 10.1016/S0042-6989(01)00002-5 · DOI
- 10.1109/TGRS.2020.3015157 · DOI
- 10.1007/s11042-021-10836-w · DOI
- 10.1016/j.procs.2017.05.025 · DOI
- ekman, 1978, Facial Action Coding System Manual
- 10.1109/ACCESS.2020.2964298 · DOI
- pramerdorfer, 2016, Facial Expression Recognition using Convolutional Neural Networks: State of the Art, arXiv 1612 02903
- yamamoto, 1998, Lip movement synthesis from speech based on Hidden Markov Models, 26, 105
- 10.1515/teme-2019-0024 · DOI
- lyons, 1998, Coding Facial Expressions with Gabor Wavelets, 1998
- 10.1109/FG.2018.00019 · DOI · Paper page
- 10.1109/ACCESS.2021.3078258 · DOI
- shahid, 2020, Contour and region harmonic features for sub-local facial expression recognition, J Vis Commun Image Represent
- 10.1073/pnas.1322355111 · DOI · Paper page
- 10.3390/s20041087 · DOI
Cited by
Sorted by citations
EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge
Palash, M., & Bhargava, B. (2023) · arXiv · doi:10.48550/arxiv.2306.08657 · Cited by 25
“To this end, in [15], the authors proposed an explainable pipeline utilizing facial action units and an agnostic LIME model to visualize the active regions of the face.”
Background
Metadata
- Authors
- Deramgozin, M., Jovanovic, S., Rabah, H., & Ramzan, N.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- University of the West of Scotland (GB)
- Venue
- 2021 IEEE International Conference on Imaging Systems and Techniques (IST)
- Publisher
- IEEE
- Published
- 2021-08-24
- Pages
- 1-5
- OpenAlex
- W4205425967
- Cited by
- 22
- References
- 2
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 7 · 2022: 1
- FWCI
- 1.85
- Type
- conference paper
- Language
- en
- Open access
- Licence
- Locations
- IEEE International Conference on Imaging Systems and Techniques (IST) (publishedVersion)
- The UWS Academic Portal (University of the West of Scotland) (publishedVersion, other-oa)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- facial expression recognition, explainable AI, black-box models, lime, facial action units, convolutional neural networks, CK+ dataset, model interpretability
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